Word Embeddings: A Survey
This is an incremental survey that provides a comprehensive overview of word embedding methods for researchers and practitioners in natural language processing.
The paper surveys recent strategies for constructing word embeddings, which are fixed-length, dense, and distributed representations of words based on the distributional hypothesis, and notes their utility in encoding syntactic and semantic information and enhancing downstream NLP tasks.
This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra features in many downstream NLP tasks.